Pipeline and vessel structures used in the oil and gas industry are exposed to stresses over time that can accumulate to produce defects in the structure. Unfortunately, it is typically difficult to determine whether such structures are being subjected to damaging stresses until easily observable defects occur.
The availability of non-destructive inspection techniques for structural materials, for instance, nonmetallic pipes used in pipelines, is limited. For the most part, the techniques available so far are either destructive to the material or are experimental and unreliable. Moreover, with respect to the existing image processing systems for detecting features within an image of an inspected surface typically concentrate on averaging, smoothing and isolating features based on frequency. Generally, existing non-destructive systems and monitoring techniques for inspection of materials can be inadequate for efficiently detecting the presence of stresses on or in the material such as tensile stress or compressive stress with sufficient accuracy and precision such that defects can be predicted before they occur.
Surface approximation using computer graphics technologies has been utilized to process image data sets. There exist several methods of surface approximation including, square/rectangle grid approximation that uses smaller squares to discretize a large surface domain, triangular regular network that uses triangles of the same shape, triangular irregular network that uses triangles of any shape and the like. However, current computer technologies and algorithms for surface approximation can require massive digital data storage and expensive computer hardware because of the complexity of the processing algorithms, and monitoring systems.
What is needed is a system and method for efficiently detecting features from surface-image data through computer image processing with sufficient resolution and accuracy. More specifically, an image processing and monitoring system which isolates features and can be used to quantify material deformation of structures from image data collected using optical inspection devices.
It is with respect to these and other considerations that the disclosure made herein is presented.